Mendelian Randomization Analysis: LDL Cholesterol and Alzheimer's Disease

By Dr. Shea Andrews, generated on 2018-08-23

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Data sources

LDL Cholesterol Willer et al Nat Genet 2013: To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10−8, including 62 loci not previously associated with lipid levels in humans.

Late Onset Alzheimer’s disease (LOAD, Lambert et al 2013): The International Genomics of Alzheimer’s Project (IGAP) is a meta-analysis of 4 previously published GWAS datasets: the European Alzheimer’s Disease Imitative (EADI), the Alzheimer Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), and Genetic and Environmental Risk in AD (GERAD) and includes a sample of 17,008 LOAD cases and 37,154 cognitively normal elder controls. Participants in IGAP were of European ancestry, the average age was 71 and 58.4% of participants were women.

Alzheimer’s Age of Onset Surivial (AAOS, Huang et al 2017): A GWAS of age of onset in LOAD was conducted in 14,406 AD case samples and 25,849 control samples from the IGAP using Cox proportional hazard regressions. Participants were of European ancestry, in cases the the average AAO was 74.8 and 61.7% were women, in controls the average AAE was 79.0 and 59.6% were women.

CSF Ab42, tau & ptau (AB42, ptau, tau, Deming et al 2017): A GWAS of CSF AB42, ptau and tau levels (pg/mL) was conducted in 3,146 participants. Participants were of Eurpean ancestry.

Instrumental Vaibles

LD Clumping: For standard two sample MR it is important to ensure that the instruments for the exposure are independent. LD clumping can be performed using the data_clump function from the TwoSampleMR package, which uses EUR samples from the 1000 genomes project to estimate LD between SNPs and amonst SNPs that have and LD above a given threshold, only the SNP with the lowest p-value will be retained.

Proxy SNPs: SNPs associated with LDL Cholesterol were extracted from the GWAS of LOAD, AAOS, AB42, ptau and tau. Where SNPs were not available in the outcome GWAS, the EUR thousand genomes was queried to identified potential proxy SNPs that are in linkage disequilibrium (r2 > 0.8) of the missing SNP.

LDL Cholesterol

LDL Cholesterol: 3060 SNPs (Table 1) were assoicated with were associated with LDL Cholesterol at p < 5e-8. After LD clumping, 2842 of 3060 SNPs were removed.

Table 1: Independent SNPS associated with LDL Cholesterol

LOAD

Of the the 218 SNPs associated with LDL Cholesterol, 199 were available in the LOAD GWAS (Table 2).

Table 2: SNPS associated with LDL Cholesterol avalible in LOAD GWAS

AAOS

Of the the 218 SNPs associated with LDL Cholesterol, 213 were available in the AAOS GWAS (Table 3).

Table 3: SNPS associated with available in AAOS GWAS

AB42

Of the the 218 SNPs associated with LDL Cholesterol, 162 were available in the CSF AB42 GWAS (Table 4).

Table 4: SNPS associated with LDL Cholesterol avalible in ab42 GWAS

Ptau

Of the the 218 SNPs associated with LDL Cholesterol, 185 were available in the CSF Ptau GWAS (Table 5).

Table 5: SNPS associated with LDL Cholesterol avalible in Ptau GWAS

Tau

Of the the 218 SNPs associated with LDL Cholesterol, 162 were available in the CSF Tau GWAS (Table 6).

Table 6: SNPS associated with LDL Cholesterol avalible in tau GWAS

Data harmonization

Harmonize the exposure and outcome datasets so that the effect of a SNP on the exposure and the effect of that SNP on the outcome correspond to the same allele. The harmonise_data function from the TwoSampleMR package can be used to perform the harmonization step, by default it try’s to infer the forward strand alleles using allele frequency information. EAF were not availbe in the IGAP summary statisitics, as such the allele frequencies reported in the AAOS anaylsis were used.

LDL Cholesterol ~ LOAD

Table 7: Harmonized LDL Cholesterol and LOAD datasets

LDL Cholesterol ~ AAOS

Table 8: Harmonized LDL Cholesterol and AAOS datasets

LDL Cholesterol ~ AB42

Table 9: Harmonized LDL Cholesterol and AB42 datasets

LDL Cholesterol ~ Ptau

Table 10: Harmonized LDL Cholesterol and Ptau datasets

LDL Cholesterol ~ Tau

Table 11: Harmonized LDL Cholesterol and Tau datasets

Pleiotropy

Pleiotropy was assesed using Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), that allows for the evlation of evaluation of horizontal pleiotropy in a standared MR model. MR-PRESSO performs a global test for detection of horizontal pleiotropy and correction of pleiotropy via outlier removal.

LDL Cholesterol ~ LOAD: The MR-PRESSO global test for pleiotropy was significant (p = <5e-05). The following SNPs were removed due to pleiotropy: rs10419669, rs1048699, rs11485618, rs12208357, rs12410656, rs1367117, rs157580, rs1883025, rs2075650, rs2927477, rs2965101, rs2972564, rs3125055, rs3852861, rs4803763, rs4970834, rs515135, rs585131, rs6504872, rs6511720, rs6544713, rs71352239, rs75687619, rs8103315

LDL Cholesterol ~ AAOS: The MR-PRESSO global test for pleiotropy was significant (p = <5e-05). The following SNPs were removed due to pleiotropy: rs1048699, rs10947332, rs1531517, rs157580, rs2075650, rs2965101, rs3852861, rs4803763, rs515135, rs6511720, rs71352239, rs75687619, rs8103315

LDL Cholesterol ~ AB42: The MR-PRESSO global test for pleiotropy was significant (p = <5e-05).. The following SNPs were removed due to pleiotropy: rs1048699, rs10947332, rs12410656, rs157580, rs2075650, rs2927477, rs2972564, rs3852861, rs4803763, rs71352239, rs7254892, rs75687619, rs8103315

LDL Cholesterol ~ Ptau: The MR-PRESSO global test for pleiotropy was significant (p = <5e-05).. The following SNPs were removed due to pleiotropy: rs10947332, rs157580, rs2075650, rs2972564, rs3852861, rs4803763, rs71352239, rs75687619, rs8103315

LDL Cholesterol ~ Tau: The MR-PRESSO global test for pleiotropy was significant (p = <5e-05).. The following SNPs were removed due to pleiotropy: rs11066028, rs16831243, rs2194562, rs3184504, rs4142393, rs4942486, rs7552841, rs7953150, rs9875338

Mendelian Randomization Analysis

To obtain an overall estimate of causal effect, the SNP-exposure (Major Depressive Disorder) and SNP-outcome coefficients (Alzheimer’s disease and Alzheimer’s Age of Onset) were combined in 1) a random-effects meta-analysis using an inverse-variance weighted approach (IVW); 2) a Weighted Median approach; 3) and Egger Regression. IVW is equivalent to a weighted regression of the SNP-outcome coefficients on the SNP-exposure coefficients with the intercept constrained to zero. This method assumes that all variants are valid instrumental variables based on the Mendelian randomization assumptions. The causal estimate of the IVW analysis expresses the causal increase in the outcome (or log odds of the outcome for a binary outcome) per unit change in the exposure. Weighted median MR allows for 50% of the instrumental variables to be invalid. MR-Egger regression allows all the instrumental variables to be subject to direct effects (i.e. horizontal pleiotropy), with the intercept representing bias in the causal estimate due to pleiotropy and the slope representing the causal estimate.

LDL Cholesterol ~ LOAD

Figure 1 illustrates the SNP-specific associations with LDL Cholesterol versus the association between each SNP and risk of LOAD.

Fig. 1: Scatterplot of SNP effects for the association of Trait and LOAD

Figure 2 and Table 1 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted LDL Cholesterol on risk of LOAD.

Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations

Table 1: MR estimates for LDL Cholesterol and LOAD

Figure 3 shows a funnel plot to detect pleiotropy and Table 2 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.

Fig. 3: Funnel plot of the Trait – LOAD causal estimates against their precession

Table 2: Heterogenity tests for LDL Cholesterol and LOAD

## Warning in mr_heterogeneity(trait_LOAD.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.

Table 3: Test for directional pleitropy for LDL Cholesterol and LOAD

LDL Cholesterol ~ AAOS

Figure 4 illustrates the SNP-specific associations with LDL Cholesterol versus the association between each SNP and AAOS.

Fig. 4: Scatterplot of SNP effects for the association of Trait and AAOS

Figure 5 and Table 4 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted LDL Cholesterol on AAOS.

Fig. 5: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations

Table 4: MR estimates for LDL Cholesterol and AAOS

Figure 6 shows a funnel plot to detect pleiotropy and Table 5 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.

Fig. 6: Funnel plot of the traitohol Conumption – AAOS causal estimates against their precession

Table 5: Heterogenity tests for LDL Cholesterol and AAOS

## Warning in mr_heterogeneity(trait_AAOS.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.

Table 6: Test for directional pleitropy for LDL Cholesterol and AAOS

LDL Cholesterol ~ AB42

Figure 1 illustrates the SNP-specific associations with LDL Cholesterol versus the association between each SNP and AB42.

Fig. 1: Scatterplot of SNP effects for the association of trait and AB42

Figure 2 and Table 7 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted LDL Cholesterol CSF AB42 levels.

Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations

Table 7: MR estimates for LDL Cholesterol and AB42

Figure 3 shows a funnel plot to detect pleiotropy and Table 8 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.

Fig. 3: Funnel plot of the trait – AB42 causal estimates against their precession

Table 8: Heterogenity tests for LDL Cholesterol and AB42

## Warning in mr_heterogeneity(trait_AB42.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.

Table 9: Test for directional pleitropy for LDL Cholesterol and AB42

LDL Cholesterol ~ Ptau

Figure 1 illustrates the SNP-specific associations with LDL Cholesterol versus the association between each SNP and risk of Ptau.

Fig. 1: Scatterplot of SNP effects for the association of trait and Ptau

Figure 2 and Table 9 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted LDL Cholesterol on risk of Ptau.

Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations

Table 9: MR estimates for LDL Cholesterol and Ptau

Figure 3 shows a funnel plot to detect pleiotropy and Table 10 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.

Fig. 3: Funnel plot of the trait – Ptau causal estimates against their precession

Table 10: Heterogenity tests for LDL Cholesterol and Ptau

## Warning in mr_heterogeneity(trait_Ptau.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.

Table 11: Test for directional pleitropy for LDL Cholesterol and Ptau

LDL Cholesterol ~ Tau

Figure 1 illustrates the SNP-specific associations with LDL Cholesterol versus the association between each SNP and CSF Tau levels.

Fig. 1: Scatterplot of SNP effects for the association of trait and Tau

Figure 2 and Table 12 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted LDL Cholesterol on risk of Tau.

Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations

Table 12: MR estimates for LDL Cholesterol and Tau

Figure 3 shows a funnel plot to detect pleiotropy and Table 13 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.

Fig. 3: Funnel plot of the trait – Tau causal estimates against their precession

Table 14: Heterogenity tests for LDL Cholesterol and Tau

## Warning in mr_heterogeneity(trait_Tau.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.

Table 15: Test for directional pleitropy for LDL Cholesterol and Tau

MR analysis results